Transcript MCLEOD - Gunadarma University

CHAPTER 8
INFORMATION IN ACTION
Management Information Systems, 9th edition,
By Raymond McLeod, Jr. and George P. Schell
© 2004, Prentice Hall, Inc.
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Learning Objectives
• Recognize that the transaction processing system processes
data that describes the firm's basic daily operations.
• Become familiar with the processes performed by a
transaction processing system for a distribution firm.
• Recognize that organizational information systems have
been developed for business areas and organizational levels.
• Understand the processes performed by a marketing
information system.
• Understand the processes performed by a human resources
information system.
• Know the basic architecture of an executive information
system.
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Learning Objectives (cont.)
• Know what customer relationship management is and why
it requires a large computer storage capability.
• Know how a data warehouse differs from a database.
• Know the basic architecture of a data warehouse system.
• Know how data is stored in a data warehouse.
• Know how a user navigates through a warehouse data
repository.
• Know what on-line application processing is.
• Know the two basic ways to engage in data mining.
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Introduction
• This chapter gives examples of how information is
used in today's firms
• Transaction Processing Systems process data that
describe the firm's daily operations and produce a
database used by other firm systems
• A related application is Customer Relationship
Management (CRM)
• CRM uses data warehousing, meaning data
accumulates over time and can retrieved for use in
decision making
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THE TRANSACTION
PROCESSING SYSTEM
• This term TPS is used to describe the IS that gathers
data describing the firm’s activities, transforms the
data into information, and makes the information
available to users both inside and outside the firm
• Figure 8.1 is a model of a TPS where data is gathered
from the firm’s physical system and environment, and
entered into a database
• Data processing software transforms the data into
information for the firm’s management and for
individuals and organizations in the firm’s
environment
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System Overview
• Data flow diagrams (DFDs) are used to
document the system in a hierarchical
manner
• The diagram in Figure 8.2 represents the
highest level, called a context diagram
because it presents the system in the context
of its environment
• The data flowing from the distribution
system to management consists of the
standard accounting reports
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The Major Subsystems of the
Distribution System
• While context diagrams define the system
boundary, other DFDs are used to describe the
major subsystems in the firms data processes
• When a series of DFDs are used in a hierarchy,
they are called leveled DFDs
• Figure 8.3 which is a Figure 0 diagram showing
three major subsystems
• These subsystems are identified by the numbered
upright rectangles in Figure 8.3
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Systems That Fill Customer Orders
• Figure 8.4 shows the four main systems involved in
filling customer orders:
– The order entry system enters customer orders
into the system
– The inventory system maintains the inventory
records
– The billing system prepares the customer
invoices, and
– The accounts receivable system collects the
money from the customers
• Figure 8.4 expands Process 1 shown in the Figure
0 diagram, and is called a Figure 1 diagram
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Systems That Order
Replenishment Stock
• The subsystems concerned with ordering
replenishment stock from suppliers are shown
in Figure 8.5, which is called a Figure 2
diagram since it explodes Process 2 of the
Figure 0 diagram
– The purchasing system issues purchase orders to
suppliers for the needed stock
– The receiving system receives the stock, and
– The accounts payable system makes payment
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Systems That Perform
General Ledger Processes
• Figure 8.6 shows the detail for the last of the three
processes in the Figure 0 diagram
• The general ledger system is the part of the
accounting system that combines data from other
accounting systems to present a composite financial
picture of the firm. Two subsystems are involved:
– The update general ledger system posts records
that describe the various actions and transactions to
the general ledger
– The prepare management reports system uses the
contents of the general ledger to prepare the balance
sheet and income statement
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ORGANIZATIONAL
INFORMATION SYSTEMS
• Other specialized information systems used in a
firm include the marketing information system
(MKIS) and the human resources information
system (HRIS)
• Another IS that is implemented at the
organizational level is the executive information
systems (EIS), used by upper level managers in an
organization
• The MKIS, HRIS, and EIS are described below.
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The Marketing Information System
• An MKIS is made up of input and output subsystems
connected by a database (Figure 8.7)
• The Input Subsystems are:
• Transaction processing system
• The marketing research subsystem
• The marketing intelligence subsystem
• Each output subsystem provides information about
four critical elements in the marketing mix:
–
–
–
–
The product subsystem
The place subsystem
The promotion subsystem
The price subsystem
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The Human Resources
Information System
• Figure 8.8 illustrates the human resources information
system (HRIS)
• The figure shows three main HRIS input subsystems:
– The transaction processing system provides input data
– The human resources research subsystem used for
gathering specialized research information
– The human resources intelligence subsystem that
gathers environmental data that bears on HR issues
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The Executive Information System
• The executive information system (EIS) provides
information to top-level managers on overall firm
performance.
• A firm's EIS usually includes executive workstations
networked to a central server (shown in Figure 8.9)
• Some executives prefer more detail, so EIS designers
build in flexibility so their systems fit the preferences
of all executives, whatever they are
• One approach is to provide a drill-down capability,
giving executives the ability to bring up a summary
display and then display successively greater levels of
detail (Figure 8.10)
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CUSTOMER RELATIONSHIP
MANAGEMENT (CRM)
• CRM systems are used to manage relationships
between a firm and its customers so both can receive
maximum value from the relationship
• Using more effort to cultivate long-term client
relationships makes good marketing sense since its
usually cheaper to keep existing customers than to
obtain new ones
• The CRM system accumulates customer data over a
long period and uses the data to produce information
for users. A CRM system’s central element is the data
warehouse
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DATA WAREHOUSING
• Until recently, computer technology could not
support a system with such large-scale data
demands
• The term data warehouse was coined to describe a
data store with the following characteristics:
– Very large scale storage capacity
– The data is accumulated into new records
instead of updating existing records with new
information
– The data is easily retrievable.
– The data is used for decision making, not for the
firm's daily operations
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The Data Warehousing System
• A data warehousing system (Figure 8.11) enters
data into the warehouse, transforms the data into
information, and makes the information available
to users
• Data is gathered from data sources and goes
through a staging area before being entered in the
warehouse data repository
• An information delivery system obtains data from
the warehouse data repository and transforms it
into information for the users
• The data warehousing system also includes a
management and control components
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How Data Is Stored in the
Warehouse Data Repository
• The warehouse data repository stores two types of
data in separate tables, which are combined to
produce an information package
• Identifying and descriptive data are stored in
dimension tables (Figure 8.12)
• Fact tables contain the quantitative measures of an
entity, object, or activity (Fig. 8.13)
• An information package identifies all of the
dimensions that will be used in analyzing a
particular activity. Figure 8.14 shows the format
and Figure 8.15 includes some sample data
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Figure 8.14
Information
Package Format
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Figure 8.15 A Sample
Information Package
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The Star Schema
• The key that identifies the dimension and provides
the link to connect the dimension tables to the fact
table is called a star schema
• Figure 8.16 shows how the keys in four dimension
tables are related to keys in the information
package in the center
• Fig. 8.17 is an example using the four dimension
tables: customer, time, salesperson, and product
• The warehouse data repository contains multiple
star schemas – one for each activity type to be
analyzed
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INFORMATION DELIVERY
• The final element in the data warehousing system
is the information delivery system
• Information is obtained from the data repository,
transformed into information, and made available
to users
• Figure 8.18 shows how the user can navigate the
data repository to produce summary information,
detailed information, and detailed data
• Figure 8.19 shows the results of a drill-across
navigation, producing outputs in different
hierarchies
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ONLINE ANALYTCAL PROCESSING
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•
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OLAP is a type of software especially developed for
data warehouses
Using OLAP, users can communicate with the data
warehouse either through a GUI or Web interface, and
quickly produce information in a variety of forms,
including graphics
There are two approaches to OLAP (Figure 8.20):
1. ROLAP (for relational online analytical processing) that
utilizes a standard relational DBMS
2. MOLAP (for multidimensional online analytical
processing) that utilizes a special multidimensional DBMS
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ROLAP and MOLAP
• Both OLAP types include a data warehouse server and
a second server that houses OLAP software
• A major difference is that the MOLAP workstation
includes a downloaded multidimensional database
• The data in this database has already been formatted in
various dimensions so that it may be made available
quickly rather than go through time-consuming
analyses
• Figure 8.21 illustrates a report that is the type that
ROLAP can easily prepare
• MOLAP can produce information in many dimensions
• Figure 8.22 illustrates a summary report in four
dimensions: store type, product, age, and gender 42
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DATA MINING
• Data mining is the process of finding relationships
in data previously unknown to the user
• Data mining helps users discover relationships and
present them in an understandable way so the
relationships can be used in decision making
• The two basic data mining techniques are:
– Hypothesis Verification where data is used to
test theories
– Knowledge Discovery in which users search
for common characteristics within the data
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END OF CHAPTER 8
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